




'''simple docstring'''


import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    'split_dict'					,       [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'					,       num_bytes=1337					,       num_examples=42					,       dataset_name='my_dataset'      )}      ),
        SplitDict({'train': SplitInfo(name='train'					,       num_bytes=1337					,       num_examples=42      )}      ),
        SplitDict({'train': SplitInfo()}      ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				SplitDict      ):
  __a								=       split_dict._to_yaml_list()
  assert len(a_      ) == len(a_      )
  __a								=       SplitDict._from_yaml_list(a_      )
  for split_name, split_info in split_dict.items():
    # dataset_name field is deprecated, and is therefore not part of the YAML dump
    __a								=       None
    # the split name of split_dict takes over the name of the split info object
    __a								=       split_name
  assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'					,       [SplitInfo(), SplitInfo(dataset_name=a_      ), SplitInfo(dataset_name='my_dataset'      )]      )
def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  # For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
  # field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
  __a								=       asdict(SplitDict({'train': split_info}      )      )
  assert "dataset_name" in split_dict_asdict["train"]
  assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name









'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase			):
   _a    						=   StableUnCLIPImgaImgPipeline
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _a    						=   frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _a    						=   frozenset([]			)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       32
     __a								=       embedder_hidden_size

     # image encoding components

     __a								=       CLIPImageProcessor(crop_size=32   ,				size=32   )

     torch.manual_seed(0   )
     __a								=       CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=UpperCamelCase   ,				projection_dim=UpperCamelCase   ,				num_hidden_layers=5   ,				num_attention_heads=4   ,				image_size=32   ,				intermediate_size=37   ,				patch_size=1   ,				)   )

     # regular denoising components

     torch.manual_seed(0   )
     __a								=       StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase   )
     __a								=       DDPMScheduler(beta_schedule='squaredcos_cap_v2'   )

     torch.manual_seed(0   )
     __a								=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

     torch.manual_seed(0   )
     __a								=       CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=UpperCamelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)   )

     torch.manual_seed(0   )
     __a								=       UNetaDConditionModel(
         sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')   ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type='projection'   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=UpperCamelCase   ,				layers_per_block=1   ,				upcast_attention=UpperCamelCase   ,				use_linear_projection=UpperCamelCase   ,				)

     torch.manual_seed(0   )
     __a								=       DDIMScheduler(
         beta_schedule='scaled_linear'   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type='v_prediction'   ,				set_alpha_to_one=UpperCamelCase   ,				steps_offset=1   ,				)

     torch.manual_seed(0   )
     __a								=       AutoencoderKL()

     __a								=       {
         # image encoding components
         'feature_extractor': feature_extractor,
         'image_encoder': image_encoder.eval(),
         # image noising components
         'image_normalizer': image_normalizer.eval(),
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder.eval(),
         'unet': unet.eval(),
         'scheduler': scheduler,
         'vae': vae.eval(),
     }

     return components







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=0   ,				UpperCamelCase=True   )					->     Dict:
     if str(UpperCamelCase   ).startswith('mps'   ):
       __a								=       torch.manual_seed(UpperCamelCase   )
     else:
       __a								=       torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )

     __a								=       floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

     if pil_image:
       __a								=       input_image * 0.5 + 0.5
       __a								=       input_image.clamp(0   ,				1   )
       __a								=       input_image.cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()
       __a								=       DiffusionPipeline.numpy_to_pil(UpperCamelCase   )[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }







   @skip_mps
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       'cpu'  # ensure determinism for the device-dependent torch.Generator
     __a								=       self.get_dummy_components()
     __a								=       StableUnCLIPImgaImgPipeline(**UpperCamelCase   )
     __a								=       sd_pipe.to(UpperCamelCase   )
     sd_pipe.set_progress_bar_config(disable=UpperCamelCase   )

     __a								=       self.get_dummy_inputs(UpperCamelCase   )
     inputs.update({'image_embeds': None}   )
     __a								=       sd_pipe(**UpperCamelCase   ).images
     __a								=       image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __a								=       np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase   )







   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()   ,				reason='XFormers attention is only available with CUDA and `xformers` installed'   ,				)
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase   )





@slow
@require_torch_gpu
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-l-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     __a								=       pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       pipe(
         UpperCamelCase   ,				'anime turtle'   ,				num_inference_steps=2   ,				output_type='np'   ,				)

     __a								=       torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



